 And pass the baton to Chris. Thank you if we could have the questions come up. Basically what I did is to clone the question slides that were in Josh's presentation. Next slide please. And these are the questions that had emerged. Next slide. And these are more or less his answers. So I think he's explained to these. But the points to emphasize are how do we really make the process better and faster? And that synergizes very closely with Dan's point of how do we make this a scalable environment? I mean to what degree can we engage academic medical centers, or for that matter, medical centers, academic or otherwise, to have a very low barrier of entry of sharing. It really comes down to the big data sharing problem that NIH is looking at very carefully, as we all know. But I think Josh did hit on many of the issues. Many of you who know me will know that standards and extensible methods are by middle name, and the whole notion of the EMR, which is an evolving target culturally and technically in most of our organizations, is actually having increasing capabilities. Next slide. I tried to synthesize Dan's question. So this ultimate reductionist model, Dan, forgive me. So we've already talked about what would it take to scale. And I was very intrigued with Dan's mention of the LEGO pieces because, of course, this reflects the clinical element modeling world and the whole clinical information modeling initiative that Dan Huff is leading, and the creation, if you will, of modular data elements that can be aggregated into modular observations. So the notion of hypertension as a really computable observation as a component of more complex phenotypes or any permutations, as you said. And then, of course, you went into the decision support problem, and you explicitly included curly braces. I don't know if you noticed that. At least in the decision support world, the old joke was the decision support is great as long as you can solve the curly braces problem, which means a miracle occurs here. You map your own local organization's information to whatever the author of that decision support rule had intended, and this was, of course, done through divine intervention. So we have panelists that I don't want to overlook in this discussion that were named and designated. And those are, if participants don't mind, we'll turn to the panelists first for further discussion and then open it to general discussion. They're Jeraldo Heiss, Stan Huff, and Zach Gawane. So why don't we take them in that order. Jeraldo, are you on the line? I am, and just unmuted myself. Thank you. Fascinating. Thank you. I want to perhaps point to one, many, many points. Excellent points have been raised. Perhaps one that intrigued me in particular is the notion that at what point are we ready to cross the watershed between phenotype development toward implementation in the sense of readiness as these algorithms have been developed. They have done a superb job in supporting association studies and discovery. At that point, I think we have evidence that they do work and the group has done a superb job. As we approach the question of implementation, in fact, aren't already jumping into it. What are the costs of failing to appropriately optimize, say, predictive value or something like that? Is that sufficient to support clinical decision making? Is it sufficient to support reporting to participants and so forth? In other words, what is the cost of less than perfect, but ladies when it comes to these EHR-based, record-based phenotypes that perform so well when it comes to association studies, perhaps possibly reducing a little bit the power, the statistical power at that level. But what assurance do we have to have in place to move toward implementation and the next applications of these algorithms? Things that were approached in a general sense and that perhaps require a little more thought in terms of specificity. That's all I have to say. Thank you very much, Dan. Some of this probably goes back to the previous discussion. I actually have more questions than I have conclusions. We're working and in terms of making a lot of these things actual practical and workable, for instance, the commercial labs that we're ordering our genetic tests from aren't set up to send back coded and structured data so that we can incorporate it in a computable form in our record. What we typically get back electronically is something that says deprinted report. It's faxed to us as an image document rather than even as a partial document. Another part of that, again, just sort of practical plumbing kind of things are improving our test ordering systems so that we get appropriate family history and historical data and clinical data available to the laboratories when they do the testing so that you get better testing. Then another thing that I think is interesting to think about is there's a motivation and there have been pre-agreement by this group about sharing of data and when I get back to my own sort of state and institution, it's clear that this kind of sharing of data is going to improve research and also going to improve patient care. What's not clear or seems to be a problem in a sense is even though that's true, the local politics of competition between healthcare organizations and other things aren't conducive to people actually sharing data. At some level, everybody understands its best. I wonder, again, what people have thought or seen or ways to create better incentives for people to actually do the data sharing within a community. Let's move to Zach, if we can. Can we go to the last slide? This is my simplistic summary and points of emphasis. No, we can't, Zach. Sorry, your message popped up. What degree of clinical normalization is needed and will, meaningfully, use standards to help us? Oh, there you are, Zach. Let me just finish this and then we'll let you continue. Then, finally, culturally within an organization, if we're going to achieve the kinds of scalability and low cost for phenotyping, organizations and medical centers, starting to happen with accountable care organizations like Heavens, need to think of clinical data as a primary resource for it. Right now, it's almost a byproduct of the care process. Concerns about comparability, consistency, or worse, any mode of reusability are second or third tier in the context of many organizations. Zach, carry on. Yes, well, first of all, amen to what you just said, brother. That's exactly right. And it actually dovetails exactly with what I was going to say. So I think the point that was just made about the laboratory vendors is valid, but it is dwarfed by the problem of the electronic health record system vendors are not particularly exercised to either represent the family history or genomic data in the electronic health record systems. And I will not name the individuals in our group who reported that their vendors, their commercial vendors, were now a couple years behind what they had promised where they would be when they first sold them these systems in regard to genomics. But, and here's my real point. My point is it comes back to us and it comes back to Chris's point. If our clinical leadership does not make this genomic and genetic decision support part of their primary criteria for selecting electronic health record systems, we will be waiting many, many years. And no matter what we do here in Emerge, we'll be working at the margins if our clinical leadership does not make that selection feature a primary selection metric in choosing a vendor. And I think we just have to recognize that. Without that, we will be working at the margins. All right. Thank you, Zach. So now we have time. We still have time, don't we? For general discussion, any comments from panelists or presenters or anyone else? Yeah, this is Mark Williams. A comment and a question. A comment relates to clinical decision support and I think looking at Dan's sort of five versions of this, I just wanted to let the group know that there are actually some ongoing discussions in a couple of those areas. The infobutton project that you've already heard about, which would be a way to represent educational information related to genomics, is interacting with the open infobutton standards group and others to develop hopefully a generalizable solution that could represent genetic and genomic data. So that's number one. Number two is that the clinical decision support consortium, which had been funded through HRQ, whose funding ended last year, has been resurrected in the version 2.0 with Blackford Middleton, the original PI, leading this effort. Blackford is now at Vanderbilt, and they have been very interested and have reached out to the electronic health record integration group about using some of our eMERGE PGX use cases and clinical decision support that we're building as exemplars for their work. So we do have some nascent efforts in that area. The question that I have comes back to the LEGO model, and I'm wondering if anyone has, or if it would be possible to, define a set of basic phenotypes that we would agree could be assembled to answer some substantial proportion of clinical questions. If that is possible to do, then that could very clearly lay out prioritization of phenotypes that we would want to be able to do that would then allow the reassembly of the phenotypes in this modular way to answer a ton of clinical questions. Okay, Peggy, do you want to answer that, or just to put the mic? Yeah. With respect to the modular phenotypes, actually, Luke Rasmussen has a paper under review apropos what you were saying, applying software design patterns to phenotype design patterns and showing that there are a finite number of repeatable patterns that compose our phenotypes. So that suggests that the modularization is actually quite tractable. One other thing I wanted to comment on was with respect to the phenotypes and clinical application, one issue that we've run into a lot in eMERGE 1 and eMERGE 2 with respect to phenotypes is that the definite yeses and the definite noes are often a minority of the total patients. So Dan's comment about when do you collect more information, I think, when we go to clinical applicability will be critically important and figuring out how to structure those questions and how to build hooks into our EHRs so that our decision support, instead of being do this, becomes ask this, will be, I think, a huge step forward. Hello? If you think about the number of phenotypes, there are certain phenotypes that have been reused and the entire data set has been labeled with like diabetes, type 2 diabetes, BMI. BMI. So there are some examples of that already in play that you could imagine being extended for sure. This is Reid Peretz, can you hear me? Yes. So there's a paper in this month, Genomics and Medicine, that speaks to both phenotype ascertainment and decision support by Scott Gross and his group looking at insurance data to identify individuals in large health systems that have individual features of a pleiotropic condition, hereditary hemorrhagic telangiectasia, and by looking at individuals who have more than one of the phenotypic features but without an underlying diagnosis suggests that this condition is markedly under-diagnosed, which may be a good clinical point, but it suggests a decision support tool that could be embedded, number one, but also another way of getting at rare phenotypes in all of our databases. Anyone else? We still have about three minutes. Are we done? I'm following up on Reid's comment and a little thread that's going on in the chat room. I think the eMERGE is... I hate to use the word uniquely, but eMERGE and resources that couple very large EMRs to extensive DNA and genotypes have the potential to discover pleiotropy of the type that you describe Reid. So I think that that is an opportunity for us. I need to fix this. Somebody else is talking over me. Yeah, and that... I can't. So I would just reiterate the idea that starting to think about what variants that have minor allele frequencies of 0.1 or 0.5% due in a general population is something that we are well suited for and ought to be strongly considered as a focus. I have a question for Zach. I think if the selection of medical records when there's already taken place, revisiting that is going to be difficult, and I know you've spoken at times about these apps, which I don't know all the technical details, but at Mayo we have an eMER like that, which is basically a g-centricity on which are applied these apps and be found it very useful. And I wonder whether you could comment on that being a solution to this rather difficult problem that is in the room next to me. We're muted again, Zach. Can you hear me? Yes, we can. Good. So there's a two-point answer. I still believe that the app solution that I've talked about is a solution, and we can go along on that some other time, although we did have a webinar recently where we told you more about that. Nonetheless, I actually believe the fundamental solution is political. And here is a 0.9 or eMERGE-3. And that is, I think it would be an interesting thing of eMERGE participants and clinical leadership. Like, see, understand how their leadership position can be improved by talking to their vendors as a group. And because I think otherwise, they don't really understand the lost opportunity. And many of them are embracing this notion of precision medicine that's driven by molecular characteristics, but they don't understand that they're not driving their fundamental tools of decision support, a.k. the vendors of electronic health record systems to that point. So I would argue strongly in favor of a meeting where we actually tried to address this issue, not with the vendors, but with our clinical leadership. I put that maybe on an NIH tribute list and then a possibility wish list. So I agree strongly on that. Nothing could be more productive, I think. Could you repeat the critical issue? Because we were having trouble hearing about a little bit of that. Yeah, we were breaking up significantly that. But from what I gathered, Zach is suggesting that there's a major academic and perhaps non-academic medical centers to meet and live so that the value proposition of creating clinical data as a primary resource can be articulated and can be shared and impressed upon the vendors. And we would be in a very different world if we weren't shadow boxing with our electronic medical record, as many of us are, trying to sort of squeeze data out of that. Okay, well, that's been a very... Chris, can you hear me better now? Yes, yes, we can. Can you hear me? Okay, so it's actually... Chris, I appreciate you trying to channel me and you almost had it right. What I was suggesting is not that we meet with the electronic... our clinical leaders meet with electronic health records vendors. I was suggesting that we, the leadership of Emerge clinical leaders of the institutions, and explain to them why and what they should be demanding of the health record vendors. I don't think they even understand that right now. Important to state. I'm suggesting a joint meeting between us and our clinical leaders. Yeah, that's an important distinction. And explain what they should be demanding of medical record vendors. Yeah, thank you. Explaining the value proposition. This is Dan Roden. Terry, this is Dan Roden. This sounds like an opportunity for the Genomic Medicine Working Group. Is that enough? Yeah, that's right. So yes, so all I can say is, I know for a fact that when you actually talk to the leadership of these large clinical academic centers, they understand that this is a priority in terms of where they hope to go, and they don't understand how they have not made this a priority for their vendors. If they haven't closed that synapse, and I think by having them meet with us, their own leaders in the same space, collectively in a group works job, we could make that abundantly clear and turn that into a collective action item for them. To meet with them, I think that's a great suggestion. It'd be really important to also ask them what kind of issues are facing them first for implementation. What do they want to implement? So we're hearing from group health. They're not that interested in a lot of the pharmacogenetics that isn't really evidence-based right now. They're much more interested in highly penetrenched things for implementation, because they see that as something that's actionable right now. So I think it's really important to understand what does the health care system want from genetics and in what order? What do they think about it? So that's great. So instead of making it into a brow-beating session, we turn it into a what's important in this space and how do we use our electronic health record systems to operationalize that, but we don't make it as a technical discussion. We make it how do we turn these priorities into decisions at the C-level suite? And I think if we had the right participants, it would be a high-profile meeting. It'd be very interesting for all of us. And I think it really would move our agenda forward. And I think if we're interested in this, we can have an e-mail discussion of this. You should be invited. E-mail discussion of this. You should be invited. Okay. This is the e-mail clinic. I come out here. One thing you haven't discussed, which I think also is important in this context, is the economics. Because for our medical center, I think it's probably true for many. Genetics and genomics is looked upon as a money loser. And so I think their incentivization on this topic is going to depend on painting an image for them about how in the future this is going to have positive financial impacts. Well, I think it's an important message for us to hear by direction. If there is really no economic value, which I doubt is true, then we should hear it. Well, I'm just telling you based on our own experience of our medical center, and I think of many others so far, supporting genetics has basically been a money loser, primarily because there are procedures involved. We're trying to work on this in some fashion to at least... We refer for procedures, but we don't get the income from that. So I think from many medical centers this could potentially be an issue. Things may change as insurance, but that's why the insurance is important in this context also because as they cover more and more genetic services, things could change a bit. But there are not too many models out there where genetics is really a money-making business. Okay, so what we'll do is in order to stay on the agenda, we actually have a clear recommendation out of this meeting about convening a forum to discuss these issues. So we'll close the segment on phenotypes and move on now to EMR and genomic discovery. And for this topic, the e-merge presenter is Marilyn Ritchie. Marilyn, you have the floor.